Erratum: “Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment”

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Erratum: "Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment"

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Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment

Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from large amounts of raw text. However, state-of-the-art systems that employ suc...

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Neural Network Architectures for Prepositional Phrase Attachment Disambiguation

This thesis addresses the problem of Prepositional Phrase (PP) attachment disambiguation, a key challenge in syntactic parsing. In natural language sentences, a PP may often be attached to several possible candidates. While humans can usually identify the correct candidate successfully, syntactic parsers are known to have high error rated on this kind of construction. This work explores the use...

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Probabilistic models have been effective in resolving prepositional phrase attachment ambiguity, but sparse data remains a significant problem. We propose a solution based on similarity-based smoothing, where the probability of new PPs is estimated with information from similar examples generated using a thesaurus. Three thesauruses are compared on this task: two existing generic thesauruses an...

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ژورنال

عنوان ژورنال: Transactions of the Association for Computational Linguistics

سال: 2015

ISSN: 2307-387X

DOI: 10.1162/tacl_a_00125